# Vision-RADAR fusion for Robotics BEV Detections: A Survey

arXiv: https://arxiv.org/abs/2302.06643 · HTML: https://mlsanity.com/paper/2302.06643/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [One Thousand and One Hours: Self-driving Motion Prediction Dataset](https://mlsanity.com/paper/2006.14480/) (2020)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes](https://mlsanity.com/paper/1903.01568/) (2019)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer](https://mlsanity.com/paper/2209.06535/) (2022)
- [MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review](https://mlsanity.com/paper/2108.03004/) (2021)
- [MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis](https://mlsanity.com/paper/1805.01123/) (2018)
- [Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather](https://mlsanity.com/paper/1902.08913/) (2019)
- [BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies](https://mlsanity.com/paper/1511.06909/) (2015)
- [SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles](https://mlsanity.com/paper/2003.04447/) (2020)

## Built on by (most cited first, 14 of 14)

- [PointBeV: A Sparse Approach to BeV Predictions](https://mlsanity.com/paper/2312.00703/) (2023)
- [Transformer-Based Sensor Fusion for Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.11481/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions](https://mlsanity.com/paper/2404.14780/) (2024)
- [Trajectory-Prediction with Vision: A Survey](https://mlsanity.com/paper/2303.13354/) (2023)
- [3M3D: Multi-view, Multi-path, Multi-representation for 3D Object Detection](https://mlsanity.com/paper/2302.08231/) (2023)
- [A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2406.00714/) (2024)
- [KD360-VoxelBEV: LiDAR and 360-degree Camera Cross Modality Knowledge Distillation for Bird's-Eye-View Segmentation](https://mlsanity.com/paper/2512.15311/) (2025)
- [Training Strategies for Vision Transformers for Object Detection](https://mlsanity.com/paper/2304.02186/) (2023)
- [Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation](https://mlsanity.com/paper/2505.01615/) (2025)
- [Lidar based 3D Tracking and State Estimation of Dynamic Objects](https://mlsanity.com/paper/2304.01396/) (2023)
- [What Matters in Range View 3D Object Detection](https://mlsanity.com/paper/2407.16789/) (2024)
- [KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving](https://mlsanity.com/paper/2408.02088/) (2024)
- [Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion](https://mlsanity.com/paper/2604.25574/) (2026)

## Abstract (arXiv)

Due to the trending need of building autonomous robotic perception system, sensor fusion has attracted a lot of attention amongst researchers and engineers to make best use of cross-modality information. However, in order to build a robotic platform at scale we need to emphasize on autonomous robot platform bring-up cost as well. Cameras and radars, which inherently includes complementary perception information, has potential for developing autonomous robotic platform at scale. However, there is a limited work around radar fused with Vision, compared to LiDAR fused with vision work. In this paper, we tackle this gap with a survey on Vision-Radar fusion approaches for a BEV object detection system. First we go through the background information viz., object detection tasks, choice of sensors, sensor setup, benchmark datasets and evaluation metrics for a robotic perception system. Later, we cover per-modality (Camera and RADAR) data representation, then we go into detail about sensor fusion techniques based on sub-groups viz., early-fusion, deep-fusion, and late-fusion to easily understand the pros and cons of each method. Finally, we propose possible future trends for vision-radar fusion to enlighten future research. Regularly updated summary can be found at: https://github.com/ApoorvRoboticist/Vision-RADAR-Fusion-BEV-Survey